What you should know about this indicator
- The data shows the share of total wealth owned by different groups of the population. These wealth shares are measures of inequality: if wealth were equally distributed, each 1% of the population would own 1% of total wealth. Inequality in the distribution of wealth is present when richer groups own a more-than-equal share, and poorer groups own less. We discuss these measures and the data in more detail on our page on economic inequality.
- The data comes from the World Inequality Database (WID), and it is constructed using the Mixed Income Capitalization-Survey (MICS) method, which combines capitalized income flows from tax data with survey-based estimates for assets that do not generate taxable income. This is done because wealth distributions are usually not directly available in administrative data, and because wealth surveys coverage is even more limited than for income.
- This measure is related to household net wealth, which is the total value of non-financial (housing, land) and financial assets (deposits, bonds, equities) held by households, minus their debts.
- For some countries and years, the underlying data sources are not available. In these cases, estimates are extrapolated from other years or modeled based on data from other countries, which introduces uncertainty. For more information, see the WID methodology documentation and this technical note.
More Data on Economic Inequality
Sources and processing
This data is based on the following sources
How we process data at Our World in Data
All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.
At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.
Notes on our processing step for this indicator
We extract estimations of Gini, mean, percentile thresholds, averages, and shares via the wid Stata command. We calculate threshold and share ratios by dividing different thresholds and shares, respectively.
Interpolations and extrapolations are excluded by using the option exclude in the Stata command.
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Citations
How to cite this page
To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:
“Data Page: Wealth share of the richest 1%”, part of the following publication: Joe Hasell, Bertha Rohenkohl, Pablo Arriagada, Esteban Ortiz-Ospina, and Max Roser (2023) - “Economic Inequality”. Data adapted from World Inequality Database (WID.world). Retrieved from https://archive.ourworldindata.org/20260724-092634/grapher/wealth-share-richest-1-percent.html [online resource] (archived on July 24, 2026).How to cite this data
In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:
World Inequality Database (WID.world) (2026) – with major processing by Our World in DataFull citation
World Inequality Database (WID.world) (2026) – with major processing by Our World in Data. “Wealth share of the richest 1% – WID” [dataset]. World Inequality Database (WID.world), “World Inequality Database (WID)” [original data]. Retrieved July 24, 2026 from https://archive.ourworldindata.org/20260724-092634/grapher/wealth-share-richest-1-percent.html (archived on July 24, 2026).Download
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